The present disclosure relates generally to the field of signal processing, and more particularly to the classification of a signal against a set of pre-classified signals.
Signal classification aims to determine how likely an input (or probe) signal is to belong to particular categories or classes of signals that are already known. Typically, a signal classification system compares the input signal to a set of signals which are representative of each of the known classes of signals in order to assess the likelihood or probability that the input signal belongs to each class. These probabilities may then be used to determine a particular class that the input signal is deemed to belong to.
Standard sparse representation solvers are known which use a variety of known algorithms and techniques to solve an equation Ax=b subject to x being as sparse as possible. Such sparse representation solvers typically accept a matrix A and a vector b as inputs and output a sparse vector x as an output.